DESCRIPTION: (Applicant's Description) The research in the physics program is driven by two goals: 1) to make proton beam therapy as good as the physics of protons will allow; and 2) to make the planning, delivery and verification of proton beam therapy as efficient and therefore as cost-effective as possible. To these ends, we propose initiatives in the following areas: Improved Proton Dose Distributions: We propose to develop and implement: highly accurate isocentric treatment techniques featuring multi-segment treatments; technology for the delivery of intensity-modulated proton beam therapy through the use of pencil beam scanning; multi-leaf collimators; optimization algorithms which feature full 3D intensity-modulation using scanned beams; beam gating techniques to overcome problems of organ/target motion; and improved immobilization, treatment set-up and verification techniques. More Efficient Planning and Delivery: We propose to develop and implement: faster patient set-ups through the implementation of digital imaging technology; faster planning algorithms, automated documentation, electronic charting, patient scheduling, faster hardware, improved human interface, and beam optimization tools; and use a control system which minimizes interactions to those absolutely necessary for the execution of complex, multi-segment treatments. Improved Dosimetry & QA: We propose to develop and implement: a Monte Carlo calculation capability both for the most accurate possible computation of the dose delivered during patient treatments and for basic dosimetry; investigations of dosimetry systems; a dose-prediction algorithm which obviates the need to calibrate each field; instrumentation to efficiently and accurately test the performance of the beam delivery systems; procedures for QA and QC in order to ensure the quality and safety of treatments; a 3D dose-collection capability; and a capability within the treatment planning programs to compute detector-response distributions for non-linear detectors so that observations can be compared with prediction.